{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(1/99,99/1,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log(x)*(7.5/np.log(2))+50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.010101010101010102,\n",
       " 0.2798253494029339,\n",
       " 98.99010101010101,\n",
       " 99.72017465059706)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(x),min(y),max(x),max(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8fd1320>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = np.arange(0.01,1,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.01010101e-02, 2.04081633e-02, 3.09278351e-02, 4.16666667e-02,\n",
       "       5.26315789e-02, 6.38297872e-02, 7.52688172e-02, 8.69565217e-02,\n",
       "       9.89010989e-02, 1.11111111e-01, 1.23595506e-01, 1.36363636e-01,\n",
       "       1.49425287e-01, 1.62790698e-01, 1.76470588e-01, 1.90476190e-01,\n",
       "       2.04819277e-01, 2.19512195e-01, 2.34567901e-01, 2.50000000e-01,\n",
       "       2.65822785e-01, 2.82051282e-01, 2.98701299e-01, 3.15789474e-01,\n",
       "       3.33333333e-01, 3.51351351e-01, 3.69863014e-01, 3.88888889e-01,\n",
       "       4.08450704e-01, 4.28571429e-01, 4.49275362e-01, 4.70588235e-01,\n",
       "       4.92537313e-01, 5.15151515e-01, 5.38461538e-01, 5.62500000e-01,\n",
       "       5.87301587e-01, 6.12903226e-01, 6.39344262e-01, 6.66666667e-01,\n",
       "       6.94915254e-01, 7.24137931e-01, 7.54385965e-01, 7.85714286e-01,\n",
       "       8.18181818e-01, 8.51851852e-01, 8.86792453e-01, 9.23076923e-01,\n",
       "       9.60784314e-01, 1.00000000e+00, 1.04081633e+00, 1.08333333e+00,\n",
       "       1.12765957e+00, 1.17391304e+00, 1.22222222e+00, 1.27272727e+00,\n",
       "       1.32558140e+00, 1.38095238e+00, 1.43902439e+00, 1.50000000e+00,\n",
       "       1.56410256e+00, 1.63157895e+00, 1.70270270e+00, 1.77777778e+00,\n",
       "       1.85714286e+00, 1.94117647e+00, 2.03030303e+00, 2.12500000e+00,\n",
       "       2.22580645e+00, 2.33333333e+00, 2.44827586e+00, 2.57142857e+00,\n",
       "       2.70370370e+00, 2.84615385e+00, 3.00000000e+00, 3.16666667e+00,\n",
       "       3.34782609e+00, 3.54545455e+00, 3.76190476e+00, 4.00000000e+00,\n",
       "       4.26315789e+00, 4.55555556e+00, 4.88235294e+00, 5.25000000e+00,\n",
       "       5.66666667e+00, 6.14285714e+00, 6.69230769e+00, 7.33333333e+00,\n",
       "       8.09090909e+00, 9.00000000e+00, 1.01111111e+01, 1.15000000e+01,\n",
       "       1.32857143e+01, 1.56666667e+01, 1.90000000e+01, 2.40000000e+01,\n",
       "       3.23333333e+01, 4.90000000e+01, 9.90000000e+01])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xxx = xx/(1-xx)\n",
    "xxx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.log(xxx)*(7.5/np.log(2))+50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x900c828>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xx,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
